Ninon Burgos
109
Documents
Identifiants chercheurs
- ninon-burgos
- 0000-0002-4668-2006
- Google Scholar : https://scholar.google.co.uk/citations?user=lHuYSU0AAAAJ&hl=en
- IdRef : 25099884X
- ResearcherId : U-3404-2018
Présentation
[Ninon Burgos](https://ninonburgos.com/) is a CNRS researcher at the [Paris Brain Institute](http://icm-institute.org/) in the [ARAMIS Lab](http://www.aramislab.fr/). She completed her PhD at University College London in the [Centre for Medical Image Computing](http://www.ucl.ac.uk/medical-image-computing). She received an MSc in Biomedical Engineering from Imperial College London and an Engineering degree from a French Graduate School in Electrical Engineering and Computer Science (ENSEA). Her research currently focuses on the development of computational imaging tools to improve the understanding and diagnosis of neurological diseases.
Publications
- 56
- 28
- 24
- 24
- 21
- 20
- 18
- 18
- 14
- 13
- 11
- 11
- 10
- 10
- 10
- 10
- 9
- 9
- 9
- 9
- 9
- 8
- 8
- 8
- 7
- 7
- 6
- 6
- 6
- 6
- 6
- 6
- 6
- 6
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 7
- 10
- 15
- 8
- 7
- 9
- 11
- 15
- 8
- 12
- 6
- 1
- 6
- 2
- 2
- 2
- 2
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 12
- 11
- 10
- 10
- 9
- 9
- 8
- 7
- 7
- 6
- 5
- 4
- 4
- 4
- 4
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 38
- 26
- 2
- 2
- 2
- 1
- 1
- 1
- 1
|
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PETSPIE Medical Imaging, Feb 2024, San Diego (California), United States
Communication dans un congrès
hal-04291561v2
|
|
Recent advances in the open-source ClinicaDL software for reproducible neuroimaging with deep learningSPIE Medical Imaging, Feb 2024, San Diego, United States
Communication dans un congrès
hal-04419141v1
|
|
Generating PET-derived maps of myelin content from clinical MRI using curricular discriminator training in generative adversarial networksSPIE Medical Imaging, Feb 2024, San Diego, United States
Communication dans un congrès
hal-04362506v1
|
|
How can data augmentation improve attribution maps for disease subtype explainability?SPIE Medical Imaging, Feb 2023, San Diego, United States
Communication dans un congrès
hal-03966737v1
|
|
Simulation-based evaluation framework for deep learning unsupervised anomaly detection on brain FDG PETSPIE Medical Imaging, Feb 2023, San Diego, United States
Communication dans un congrès
hal-03835015v2
|
|
A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography TranslationBMVC 2023, 34th British Machine Vision Conference, Nov 2023, Aberdeen, United Kingdom
Communication dans un congrès
hal-04195756v2
|
|
Semi-supervised Domain Adaptation for Automatic Quality Control of FLAIR MRIs in a Clinical Data WarehouseDART 2023 - 5th MICCAI Workshop on Domain Adaptation and Representation Transfer, Oct 2023, Vancouver (BC), Canada. pp.84-93, ⟨10.1007/978-3-031-45857-6_9⟩
Communication dans un congrès
hal-04273997v1
|
|
Unsupervised anomaly detection in 3D brain FDG PET: A benchmark of 17 VAE-based approachesDeep Generative Models workshop at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), Oct 2023, Vancouver, Canada
Communication dans un congrès
hal-04185304v1
|
|
From Nipype to Pydra: a Clinica storyOHBM 2023 - Annual meeting of the Organization for Human Brain Mapping, Jul 2023, Montreal, Canada
Communication dans un congrès
hal-04278898v1
|
|
MRI field strength predicts Alzheimer's disease: a case example of bias in the ADNI data setISBI 2022 - International Symposium on Biomedical Imaging, Mar 2022, Kolkata, India. ⟨10.1109/ISBI52829.2022.9761504⟩
Communication dans un congrès
hal-03542213v1
|
|
Homogenization of brain MRI from a clinical data warehouse using contrast-enhanced to non-contrast-enhanced image translation with U-Net derived modelsSPIE Medical Imaging 2022: Image Processing, Feb 2022, San Diego, United States. pp.576-582, ⟨10.1117/12.2608565⟩
Communication dans un congrès
hal-03478798v1
|
|
ClinicaDL: an open-source deep learning software for reproducible neuroimaging processingOHBM 2022 - Annual meeting of the Organization for Human Brain Mapping, Jun 2022, Glasgow, United Kingdom
Communication dans un congrès
hal-04279014v1
|
|
Advances in the Clinica software platform for clinical neuroimaging studiesOHBM 2022 - Annual meeting of the Organization for Human Brain Mapping, Jun 2022, Glasgow, United Kingdom
Communication dans un congrès
hal-03728243v1
|
|
Clinica: an open-source software platform for reproducible clinical neuroscience studiesMRI Together 2021 - A global workshop on Open Science and Reproducible MR Research, Dec 2021, Online, France
Communication dans un congrès
hal-03513920v1
|
|
New longitudinal and deep learning pipelines in the Clinica software platformOHBM 2020 - Annual meeting of the Organization for Human Brain Mapping, Jun 2020, Montreal / Virtual, Canada
Communication dans un congrès
hal-02549242v1
|
|
Visualization approach to assess the robustness of neural networks for medical image classificationSPIE Medical Imaging 2020, Feb 2020, Houston, United States. ⟨10.1117/12.2548952⟩
Communication dans un congrès
hal-02370532v3
|
|
How serious is data leakage in deep learning studies on Alzheimer's disease classification?2019 OHBM Annual meeting - Organization for Human Brain Mapping, Jun 2019, Rome, Italy
Communication dans un congrès
hal-02105133v2
|
|
Deciphering the progression of PET alterations using surface-based spatiotemporal modelingOHBM 2019 - Annual meeting of the Organization for Human Brain Mapping, Jun 2019, Rome, Italy
Communication dans un congrès
hal-02134909v1
|
|
Beware of feature selection bias! Example on Alzheimer's disease classification from diffusion MRI2019 OHBM Annual Meeting - Organization for Human Brain Mapping, Jun 2019, Rome, Italy
Communication dans un congrès
hal-02105134v2
|
|
Prediction of future cognitive scores and dementia onset in Mild Cognitive Impairment patientsOHBM 2019 - Organization for Human Brain Mapping Conference, Jun 2019, Rome, Italy
Communication dans un congrès
hal-02098427v2
|
|
Reproducible evaluation of methods for predicting progression to Alzheimer's disease from clinical and neuroimaging dataSPIE Medical Imaging 2019, Feb 2019, San Diego, United States. ⟨10.1117/12.2512430⟩
Communication dans un congrès
hal-02025880v2
|
|
Predicting progression to Alzheimer’s disease from clinical and imaging data: a reproducible studyOHBM 2019 - Organization for Human Brain Mapping Annual Meeting 2019, Jun 2019, Rome, Italy
Communication dans un congrès
hal-02142315v1
|
|
New advances in the Clinica software platform for clinical neuroimaging studiesOHBM 2019 - Annual Meeting on Organization for Human Brain Mapping, Jun 2019, Roma, Italy. ⟨10.1016/j.neuroimage.2011.09.015⟩
Communication dans un congrès
hal-02132147v2
|
|
Comparison of DTI Features for the Classification of Alzheimer's Disease: A Reproducible StudyOHBM 2018 - Organization for Human Brain Mapping Annual Meeting, Jun 2018, Singapour, Singapore
Communication dans un congrès
hal-01758206v3
|
|
Clinica: an open source software platform for reproducible clinical neuroscience studiesAnnual meeting of the Organization for Human Brain Mapping - OHBM 2018, Jun 2018, Singapore, Singapore
Communication dans un congrès
hal-01760658v1
|
|
A pipeline for the analysis of 18F-FDG PET data on the cortical surface and its evaluation on ADNIAnnual meeting of the Organization for Human Brain Mapping - OHBM 2018, Jun 2018, Singapour, Singapore
Communication dans un congrès
hal-01757646v1
|
|
Reproducible evaluation of Alzheimer's Disease classification from MRI and PET dataAnnual meeting of the Organization for Human Brain Mapping - OHBM 2018, Jun 2018, Singapour, Singapore
Communication dans un congrès
hal-01761666v1
|
|
Using diffusion MRI for classification and prediction of Alzheimer's Disease: a reproducible studyAAIC 2018 - Alzheimer's Association International Conference, Jul 2018, Chicago, United States
Communication dans un congrès
hal-01758167v2
|
|
Three simple ideas for predicting progression to Alzheimer's disease8th International Workshop on Pattern Recognition in Neuroimaging, Jun 2018, Singapour, Singapore
Communication dans un congrès
hal-01891996v1
|
Brain volume, cerebral β-amyloid deposition, and ageing: A study of over 200 individuals born in the same week in 1946 AAIC 2017 - Alzheimer's Association International Conference, Jul 2017, London, United Kingdom. pp.P1464--P1465, ⟨10.1016/j.jalz.2017.07.534⟩
Communication dans un congrès
hal-01827188v1
|
|
Short acquisition time PET quantification using MRI-based pharmacokinetic parameter synthesisMedical Image Computing and Computer-Assisted Intervention − MICCAI 2017, 2017, Québec, Canada. pp.737--744, ⟨10.1007/978-3-319-66185-8_83⟩
Communication dans un congrès
hal-01827190v1
|
|
Exploring the population prevalence of β-amyloid burden: An analysis of 250 individuals born in mainland Britain in the same week in 1946 AAIC 2017 - Alzheimer's Association International Conference, Jul 2017, London, United Kingdom. pp.P1088--P1089, ⟨10.1016/j.jalz.2017.06.1563⟩
Communication dans un congrès
hal-01827189v1
|
|
Geometric and Dosimetric Evaluation of Three Atlas-based Segmentation Methods for Head and Neck Cancer Patients on MR ImagesMR in RT symposium, Jun 2017, Sydney, Australia
Communication dans un congrès
hal-01827193v1
|
|
|
Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's DiseaseMachine Learning in Medical Imaging 2017, Sep 2017, Quebec City, Canada. pp.8
Communication dans un congrès
hal-01578479v1
|
|
Early Diagnosis of Alzheimer’s Disease Using Subject-Specific Models of FDG-PET DataAAIC 2017 - Alzheimer's Association International Conference, Jul 2017, London, United Kingdom. pp.1-2, ⟨10.1016/j.jalz.2017.06.1618⟩
Communication dans un congrès
hal-01621383v1
|
Midlife affective symptoms are associated with lower brain volumes in later life: Evidence from a prospective UK birth cohort AAIC 2017 - Alzheimer's Association International Conference, Jul 2017, London, United Kingdom. pp.P212, ⟨10.1016/j.jalz.2017.07.086⟩
Communication dans un congrès
hal-01827192v1
|
|
A comparison of techniques for quantifying amyloid burden on a combined PET/MR scanner AAIC 2017 - Alzheimer's Association International Conference, Jul 2017, London, United Kingdom. pp.P12--P13, ⟨10.1016/j.jalz.2017.06.2276⟩
Communication dans un congrès
hal-01827194v1
|
|
|
Individual Analysis of Molecular Brain Imaging Data Through Automatic Identification of Abnormality PatternsComputational Methods for Molecular Imaging - [MICCAI 2017 Satellite Workshop], Sep 2017, Quebec City, Canada
Communication dans un congrès
hal-01567343v1
|
|
Diagnosis of Alzheimer’s Disease Through Identification of Abnormality Patterns in FDG PET Data30th Annual Congress of the European Association of Nuclear Medicine (EANM), Oct 2017, Vienna, Austria. pp.253 - 254, ⟨10.1007/s00259-017-3822-1⟩
Communication dans un congrès
hal-01632509v1
|
NiftyWeb: web based platform for image processing on the cloudScientific Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine – ISMRM 2016, May 2016, Singapore, Singapore
Communication dans un congrès
hal-01827198v1
|
|
Joint segmentation and CT synthesis for MRI-only radiotherapy treatment planningMedical Image Computing and Computer-Assisted Intervention – MICCAI 2016, Oct 2016, Athens, Greece. pp.547--555, ⟨10.1007/978-3-319-46723-8_63⟩
Communication dans un congrès
hal-01827201v1
|
|
A multi-method, multi-center study of PET/MRI brain attenuation correction on a large cohort of [18F]- FDG patients: ready for clinical implementationRSNA 216 – Annual Meeting of the Radiological Society of North America, Nov 2016, Chicago, United States
Communication dans un congrès
hal-01827203v1
|
|
CT synthesis in the head & neck and pelvic regions for radiotherapy treatment planningIPEM Workshop on MRI Guided Radiotherapy, Mar 2016, Sheffield, United Kingdom
Communication dans un congrès
hal-01827224v1
|
|
Simultaneous organ-at-risk segmentation and CT synthesis in the pelvic region for MRI-only radiotherapy treatment planningInternational Conference on the use of Computers in Radiation Therapy – ICCR 2016, Jun 2016, London, United Kingdom
Communication dans un congrès
hal-01827204v1
|
|
A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patientsIEEE Nuclear Science Symposium and Medical Imaging Conference – IEEE NSS/MIC 2016, Oct 2016, Strasbourg, France
Communication dans un congrès
hal-01827199v1
|
|
Multi atlas-based attenuation correction for brain FDG- PET imaging using a TOF-PET/MR scanner: Comparison with clinical single atlas- and CT-based attenuation correctionScientific Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine – ISMRM 2016, May 2016, Singapore, Singapore
Communication dans un congrès
hal-01827200v1
|
|
Robust CT synthesis for radiotherapy planning: Application to the head & neck regionMedical Image Computing and Computer-Assisted Intervention − MICCAI 2015, Oct 2015, Munich, Germany. pp.476--484, ⟨10.1007/978-3-319-24571-3_57⟩
Communication dans un congrès
hal-01827209v1
|
|
Evaluation of different approaches to obtain synthetic CT images for a MRI-only radiotherapy workflowMR in RT symposium, Jun 2015, Lund, Sweden
Communication dans un congrès
hal-01827206v1
|
|
Establishment of an open database of realistic simulated data for evaluation of partial volume correction techniques in brain PET/MRConference on PET/MR and SPECT/MR – PSMR 2015, May 2015, Elba, Italy. pp.A44, ⟨10.1186/2197-7364-2-S1-A44⟩
Communication dans un congrès
hal-01827207v1
|
|
|
Multi-atlas synthesis for computer assisted diagnosis: Application to cardiovascular diseasesIEEE International Symposium on Biomedical Imaging – IEEE ISBI 2015, Apr 2015, New-York, United States. pp.290--293, ⟨10.1109/ISBI.2015.7163870⟩
Communication dans un congrès
hal-01827216v1
|
CT synthesis in the head & neck region for PET/MR attenuation correction: an iterative multi-atlas approachConference on PET/MR and SPECT/MR – PSMR 2015, May 2015, Elba, Italy. pp.A31, ⟨10.1186/2197-7364-2-S1-A31⟩
Communication dans un congrès
hal-01827212v1
|
|
Partial Volume Correction of Amyvid and FDG PET data using the discrete iterative Yang techniqueAnnual Congress of the European Association of Nuclear Medicine – EANM 2015, Oct 2015, Hamburg, Germany. pp.S69, ⟨10.1007/s00259-015-3198-z⟩
Communication dans un congrès
hal-01827205v1
|
|
Subject-specific models for the analysis of pathological FDG PET dataMedical Image Computing and Computer-Assisted Intervention − MICCAI 2015, Oct 2015, Munich, Germany. pp.651--658, ⟨10.1007/978-3-319-24571-3_78⟩
Communication dans un congrès
hal-01827208v1
|
|
Detail-preserving PET reconstruction with sparse image representation and anatomical priorsInformation Processing in Medical Imaging – IPMI 2015, Jun 2015, Isle of Skye, United Kingdom. pp.540--551, ⟨10.1007/978-3-319-19992-4_42⟩
Communication dans un congrès
hal-01827210v1
|
|
Attenuation correction synthesis for hybrid PET-MR scanners: validation for brain study applicationsConference on PET/MR and SPECT/MR – PSMR 2014, May 2014, Kos, Greece. pp.A52, ⟨10.1186/2197-7364-1-S1-A52⟩
Communication dans un congrès
hal-01827222v1
|
|
Effect of scatter correction when comparing attenuation maps: Application to brain PET/MRIEEE Nuclear Science Symposium and Medical Imaging Conference – IEEE NSS/MIC 2014, Nov 2014, Seattle, United States. pp.1--5, ⟨10.1109/NSSMIC.2014.7430775⟩
Communication dans un congrès
hal-01827220v1
|
|
Joint parametric reconstruction and motion correction framework for dynamic PET dataMedical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Sep 2014, Boston, United States. pp.114-121, ⟨10.1007/978-3-319-10404-1_15⟩
Communication dans un congrès
hal-01827218v1
|
|
Simulated field maps: Toward improved susceptibility artefact correction in interventional MRIInformation Processing in Computer-Assisted Interventions – IPCAI 2014, Jun 2014, Fukuoka, Japan. pp.226--235, ⟨10.1007/978-3-319-07521-1_24⟩
Communication dans un congrès
hal-01827221v1
|
|
Image reconstruction of mMR PET data using the open source software STIRConference on PET/MR and SPECT/MR – PSMR 2014, May 2014, Kos, Greece. pp.A44, ⟨10.1186/2197-7364-1-S1-A44⟩
Communication dans un congrès
hal-01827219v1
|
|
Attenuation correction synthesis for hybrid PET-MR scannersMedical Image Computing and Computer-Assisted Intervention – MICCAI 2013, Sep 2013, Nagoya, Japan. pp.147--154, ⟨10.1007/978-3-642-40811-3_19⟩
Communication dans un congrès
hal-01827223v1
|
|
ClinicaDL: an open-source deep learning software for reproducible neuroimaging processing3IA Doctoral Workshop, Nov 2021, Toulouse, France
Poster de conférence
hal-03423072v2
|
|
Identification of unlabeled latent subtypes with saliency mapsICM welcome days, Oct 2020, Paris (online), France
Poster de conférence
hal-03365788v1
|
|
Visualization approach to assess the robustness of neural networks for medical image classificationICM days 2019, Jan 2020, Louan, France
Poster de conférence
hal-03365775v1
|
|
How serious is data leakage in deep learning studies on Alzheimer’s disease classification?Organization for Human Brain Mapping (OHBM), Jun 2019, Roma, Italy
Poster de conférence
hal-03365742v1
|
Biomedical Image Synthesis and SimulationNinon Burgos; David Svoboda. Elsevier, 2022, 978-0-12-824349-7. ⟨10.1016/C2020-0-01250-8⟩
Ouvrages
hal-03721959v1
|
|
Reproducibility in machine learning for medical imagingOlivier Colliot. Machine Learning for Brain Disorders, Springer, 2023
Chapitre d'ouvrage
hal-03957240v2
|
|
Interpretability of Machine Learning Methods Applied to NeuroimagingOlivier Colliot. Machine Learning for Brain Disorders, Springer, 2023, ⟨10.1007/978-1-0716-3195-9_22⟩
Chapitre d'ouvrage
hal-03615163v2
|
|
Introduction to medical and biomedical image synthesisNinon Burgos; David Svoboda. Biomedical Image Synthesis and Simulation, Elsevier, pp.1-3, 2022, 978-0-12-824349-7. ⟨10.1016/B978-0-12-824349-7.00008-6⟩
Chapitre d'ouvrage
hal-03721967v1
|
|
Future trends in medical and biomedical image synthesisNinon Burgos; David Svoboda. Biomedical Image Synthesis and Simulation, Elsevier, pp.643-645, 2022, 978-0-12-824349-7. ⟨10.1016/B978-0-12-824349-7.00034-7⟩
Chapitre d'ouvrage
hal-03721950v1
|
|
Neuroimaging in Machine Learning for Brain DisordersOlivier Colliot. Machine Learning for Brain Disorders, Springer, In press
Chapitre d'ouvrage
hal-03814787v1
|
|
Medical image synthesis using segmentation and registrationBiomedical Image Synthesis and Simulation, Elsevier, pp.55-77, 2022, 9780128243497. ⟨10.1016/B978-0-12-824349-7.00011-6⟩
Chapitre d'ouvrage
hal-03721697v1
|
|
Validation and evaluation metrics for medical and biomedical image synthesisBiomedical Image Synthesis and Simulation, Elsevier, pp.573-600, 2022, 978-0-12-824349-7. ⟨10.1016/B978-0-12-824349-7.00032-3⟩
Chapitre d'ouvrage
hal-03721947v1
|
|
Pseudo-healthy image reconstruction with variational autoencoders for anomaly detection: A benchmark on 3D brain FDG PET2024
Pré-publication, Document de travail
hal-04445378v1
|
|
Individualised, interpretable and reproducible computer-aided diagnosis of dementia: towards application in clinical practiceMedical Imaging. Sorbonne Université, 2022
HDR
tel-03941953v1
|